Upon binding to agonists, G protein-coupled receptors (GPCRs) mediate multiple signaling pathways by coupling to intracellular transducer proteins such as G proteins or -arrestins. These agonists, termed biased ligands, confer functional specificity to GPCRs by activating certain signaling pathways over others. Biased ligands promise precise therapeutic benefits with fewer side effects as drugs compared to today's unbiased GPCR-targeted drugs. Unfortunately due to the paucity of structural data on GPCR-transducer complexes as well as the scarcity of known biased ligands, the molecular mechanisms of biased signaling remain elusive. Obtaining experimental data on the structures of the signaling complexes of GPCRs is daunting since the GPCRs are highly dynamic and technically difficult to isolate and purify in the lab. Consequently, structure-based design of biased ligands for therapeutic and further mechanistic experimental studies has been slow. Progress in understanding the complex signaling landscape of GPCRs can be accelerated if we can increase the success and efficiency of experimental trials. Here, we propose an approach that uses a reliable and time-efficient computational method to guide and accelerate concurrent experiments to stabilize and easily purify GPCR transducer complexes. Such methods need to be developed in tandem with experimental advancements. In the short three-year R01 project our (Vaidehi, Tate and Grisshammer) collaborative efforts have resulted in unprecedented computational methods that markedly increased the understanding of the dynamics of GPCR thermostable mutants and accelerate the purification of GPCRs. The progress we have made in developing and applying novel computational methods has opened up unprecedented opportunities to expand and advance the computational toolbox to identify biasing and thermostabilizing GPCR mutants that can bias the conformations of GPCRs to stably pair with different intracellular transducer proteins, the central process in biased signaling. Building on te successes of the previous R01, we propose to advance our interdisciplinary approach with simultaneous computational method developments and experiments to (1) engineer mutant neurotensin receptor 1 (NTSR1) that shows bias signaling even with unbiased agonist, to study the biased signaling mechanisms of this peptide receptor, (2) advance the computational method LITiConDesign, to predict thermostabilizing mutations for GPCR-transducer complexes, and (3) predict thermostabilizing mutations for avian 1AR-Gs, human A2AR-Gs, 1AR--arrestin1 and A2AR--arrestin1 complexes and verify these predictions with experiments that would provide feedback to improve the computational methods. The outcome of the proposed work is a powerful computational method for routinely predicting biased and thermostable mutants of GPCR-transducer complexes. The method will also accelerate the unraveling of the mechanism of biased signaling in NTSR1 that can be extended easily to other GPCRs.